Detecting time periods associated with surgical phases and/or interventions

ABSTRACT

Surgical monitoring techniques determine various time periods associated with various surgical contexts based on information received from a patient monitor. The time periods can include start and end times and be determined by comparisons with predetermined data patterns, which can be based on heuristic and/or statistical classifications, as well as in conjunction with information received from other medical equipment. Various monitors and/or detectors and/or controllers can also operate in conjunction therewith.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/548,156, filed Oct. 10, 2006, now pending.

FIELD OF INVENTION

In general, the inventive arrangements relate to patient monitoring, andmore specifically, to monitoring patient data in various surgicalcontexts to detect surgical phases and/or surgical interventions and/orthe like.

BACKGROUND OF INVENTION

During the course of a surgical procedure (e.g., liver transplant, heartsurgery, etc.), patients are subjected to numerous surgical phasesand/or interventions as part of the surgery. For example, in any givensurgical procedure, these may include one or more of the following: i)induction, ii) intubation, iii) preparation and positioning, iv)incision, v) maintenance, vi) emergence, vii) recovery, viii)extubation, and/or ix) therapy administration, etc.

These surgical phases and interventions are well-known. For example,referring in general terms, induction commonly involves administeringintravenous and/or inhalational anesthetic agents to a patient in orderto induce a relaxed or sleepy condition and/or relieve pain in thepatient prior to or during surgery. During this phase, it is also notuncommon, for example, to attach various patient monitoring devices tothe patient, thereby allowing monitoring of the patient's breathing,oxygen levels, heart rate, blood pressure, and other bodily functions.

During this phase, a patient may also be intubated (i.e., have a tubeinserted into the patient's throat) or have an anesthetic mask securedthereto to facilitate administering the anesthetic agent to the patient.

Thereafter, the patient's body may be prepared and positioned for thesurgery, after which various incisions can be made to the patient, asappropriate and/or necessary for the given surgical procedure.

During the maintenance phase of surgery, the anesthetic agents can bemonitored and adjusted, as needed. In fact, this can often occurthroughout the entire surgical procedure.

During the emergence phase, the patient can be weaned from theanesthetic agents as the surgical procedure is completed. In some cases,reversal agents can also be administered to counteract the effects ofcertain anesthetic agents and to reduce the time it takes for thepatient to recover therefrom. In any event, the patient can be returnedto consciousness as the surgical procedures are completed.

During the recovery phase, the patient awakens, regains muscle strength,etc., ultimately returning to a normal, alert state. This can oftenoccur, for example, in a post-anesthesia or intensive care unit of ahospital ward or the like.

During either the emergence or recovery phase, it is not uncommon toextubate the patient (i.e., remove the tube from the patient's throat)or remove the anesthetic mask once the patient is able to again breatheindependently.

In any event, during these (and other) various phases and/orinterventions of surgery, and all therethroughout, patients oftenrequire constant, or near-constant, monitoring and therapyadministration. Oftentimes, for example, an intervention, such astherapy administration, including administrating a drug bolus and/oradjusting ventilator settings, etc., can coincide with measurablechanges in physiological parameters.

In any event, during the surgical procedure, it would be desirable toautomatedly detect the afore-described phases and interventions. Forexample, automatedly identifying the various surgical phases and/orinterventions can lead to increasingly intelligent and dynamic medicaldevice behavior. For example, many patient monitoring alarms can betriggered once various physiological signals cross a fixed, and commonlypredetermined, threshold. In practice, clinicians seldom adjust thesealarms from patient to patient, and they may be even less likely toadjust them from phase to phase during a particular surgery. However,the threshold for what most clinicians might categorize as normal candepend on what stage a patient is in for a particular surgery. Forexample, a heart rate of 50 beats/minute may be acceptable duringmaintenance, but it might be abnormally low during or after emergence.Or, a patient monitoring system may detect a concurrent drop in bloodpressure and heart rate followed by a concurrent rise in each. Thisinformation, combined with the elapsed time since a particular surgicalprocedure began, could denote intubation in certain contexts. As aresult, automated detection of surgical phases and/or interventions canallow threshold alarms to be appropriately dynamic throughout a givensurgical procedure.

In addition to threshold alarms, for example, rate of change alarms canalso benefit from surgical phase and/or intervention contextualinformation. For example, a rapid increase in blood pressure may benormal during intubation, but not normal during maintenance, in whichcase additional medical attention may be needed.

Furthermore, monitoring settings, such as a repeat rate for non-invasiveblood pressure cuff inflation, can also be optimized for particularsurgical contexts.

In addition, non-physiological factors can also play a role. Forexample, electrosurgical knives can induce noise in electrode dependentsignals (e.g., spikes in voltage, current, etc.) Detecting this noise,perhaps in conjunction with, for example, perceived rises in heart ratesand/or blood pressures and/or elapsed time into a surgery, could suggestthe beginning of a particular surgical incision.

In accordance with the foregoing, automated detection of varioussurgical phases and/or surgical interventions would allow sophisticatedpatient monitoring systems to interpret and/or appropriately respond topatient data in light of given surgical contexts, thereby allowing suchsystems to invoke appropriate device behavior and responses atappropriate times and enhancing the overall intelligence of medicaldevices, decision support systems, and the like.

SUMMARY OF INVENTION

In one embodiment, various surgical monitoring devices include adetector operable in electronic communication with a patient monitor todetermine various time periods associated with various surgical contextsbased on information received from the monitor. The time periods caninclude start and end times and be determined by comparisons withpredetermined data patterns, which can be based on heuristic and/orstatistical classifications, as well as in conjunction with informationreceived from other medical equipment.

In another embodiment, various surgical monitoring methods includedetermining various time periods associated with various surgicalcontexts based on information received from a patient monitor. The timeperiods can include start and end times and be determined by comparisonswith predetermined data patterns, which can be based on heuristic and/orstatistical classifications, as well as in conjunction with informationreceived from other medical equipment.

In yet another embodiment, various surgical monitoring systems include apatient monitor and detector operable in electronic communication withthe monitor to determine various time periods associated with varioussurgical contexts based on information received from the monitor. Thetime periods can include start and end times and be determined bycomparisons with predetermined data patterns, which can be based onheuristic and/or statistical classifications, as well as in conjunctionwith information received from other medical equipment.

And in yet another embodiment, various surgical monitoring systemsinclude a patient monitor; a detector operable in electroniccommunication with the monitor to determine various time periodsassociated with various surgical contexts based on information receivedfrom the monitor. The time periods can include start and end times andbe determined by comparisons with predetermined data patterns, which canbe based on heuristic and/or statistical classifications, as well as inconjunction with information received from other medical equipment; anda controller operable to invoke a response based thereon.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

A clear conception of the advantages and features constituting inventivearrangements, and of various construction and operational aspects oftypical mechanisms provided by such arrangements, are readily apparentby referring to the following illustrative, exemplary, representative,and non-limiting figures, which form an integral part of thisspecification, in which like numerals generally designate the sameelements in the several views, and in which:

FIG. 1 is a block diagram of a system for detecting surgical phasesand/or interventions;

FIG. 2 is a flow chart representing one way to develop a surgical phaseand/or intervention algorithm;

FIG. 3 depicts a fuzzy logic system that can be used with the inventivearrangements;

FIG. 4 depicts various alarm and control signals as a function ofvarious behaviors in a surgery;

FIG. 5 is a flow chart representing one way to implement the inventivearrangements; and

FIG. 6 is a graphical representation of a representative patient monitorillustrating automated detection of a patient's blood pressure and heartrate for a particular surgical phase and/or intervention.

DETAILED DESCRIPTION OF VARIOUS PREFERRED EMBODIMENTS

Referring now to the figures, preferred embodiments of the inventivearrangements will be described in terms of patient monitoring equipment.However, the inventive arrangements are not limited in this regard. Forexample, while variously described embodiments may provide protocols formonitoring patients in surgical contexts, other contexts are also herebycontemplated, including various other consumer, industrial,radiological, and inspection systems, and the like.

Now then, referring to FIG. 1, a preferred medical system 100 isdepicted for detecting various time periods associated with a surgicalprocedure and/or phases and/or interventions of a patient 110 who is, ormay soon be, subject to a particular surgical procedure. In onepreferred embodiment, the system 100 can thus comprise one or more of apatient monitor 112, a detector 114, and/or a controller 116, which canbe separate or integrated units, as desired.

For example, the patient 110 may be connected to the patient monitor 112for monitoring, displaying, and/or transmitting the patient's 110 vitalsigns, such as their blood pressure, heart rate, oxygen level, and/orother parameters, as needed or desired. In particular, the patientmonitor 112 can be used to reflect the changing conditions of thepatient 110 during the various surgical phases and/or interventions towhich the patient 110 may be subjected.

In like fashion, the patient monitor 112 is preferably in electroniccommunication with the detector 114, by techniques well-known in theart, including any wired, wireless, or combinations thereof, or anyother suitable alternatives. In any event, the detector 114 preferablyreceives data and/or other information from the patient monitor 112. Ina preferred embodiment, the detector 114 may include a stand-alonecentral processing unit (“CPU”), memory, and/or user interface (noneshown), and it can also be combined, integrated, or the like, ifdesired, with other devices as well. For example, one or more of thefollowing other medical equipment may also interconnect and/orinterrelate with the detector 114: an anesthesia machine 120, anintravenous (“IV”) pump 122, and/or an electronic record system 118,such as a Picture and Archival Computer System (“PACS”), and/or thelike.

In like fashion, the detector 114 can also be in electroniccommunication with the controller 116, again by techniques well-known inthe art, including any wired, wireless, or combinations thereof, or anyother suitable alternatives. In any event, the controller 116 preferablyreceives data and/or other information from the detector 114 and/orpatient monitor 112 and/or other medical equipment (e.g., the anesthesiamachine 120, IV pump 122, and/or electronic record system 118).

In a preferred embodiment, the detector 114 transmits output controlsignals to the controller 116 for enabling dynamic medical devicereaction throughout various surgical phases and/or interventions. Thecontroller 116, in turn, is preferably interconnected back to thepatient monitor 112 and can control the desired display and alarmparameters during the surgical procedure. It should be understood thatalthough control signals can be sent from the controller 116 to thepatient monitor 112, the system 100 can also be designed to interactwith any other alarm and/or user interface (“UI”) and/or medical devicearrangements in suitable fashion.

Preferably, the system 100, comprising various ones or combinations ofthe patient monitor 112, detector 114, and/or controller 116, can bedesigned to identify surgical phases and/or interventions with a basicconfidence. That confidence can be increased as the system's 100configuration is expanded to include, for example, the electronic recordsystem 118, anesthesia machine 120, and/or IV pump 122, as well as anyother suitable medical equipment.

Preferably, the electronic record system 118, for example, can beinterconnected to the detector 114 to provide information related tosurgical protocols, drug names used during surgery, patientdemographics, etc. Likewise, the anesthesia machine 120 can beinterconnected between the patient 110 and detector 114 to provideinformation such as ventilation readings, drug concentrations usedduring surgery, etc. The IV pump 122 can also be interconnected betweenthe patient 110 and detector 114 to provide information related toinfusion rates and the like. It should also be appreciated that thesystem 100 can also discern surgical phases and/or interventions with100 or near-100 percent confidence with manual input of surgical phasesand/or interventions to the detector 114 and/or controller 116, as by aclinician 124 or the like, so as to permit overriding an automatedresponse, for example, preferably manually.

In one preferred embodiment, the detector 114 can be either a heuristicclassifier (based on an expert system) or a statistical classifier. Aheuristic classifier mimics procedures used by an expert (e.g., ananesthesiologist) to discern surgical phases and/or interventions. Forexample, a heuristic classifier can use programmed rules to comparelocal features with expert-determined thresholds and determine whether atransition from one phase and/or intervention to the next has occurred.These programmed rules can be stored as process parameters in a memory(not shown). In any event, suitable heuristic classifiers can be used.

In contrast to the afore-described heuristic classifiers, which can relyupon expert-defined rules, statistical classifiers can develop their ownclassification rules during a training phase. Statistical classifiersmay use, for example, techniques of multivariate regression, k-nearestneighbor procedures, discriminate analysis, as well as neural networktechniques. Using local features drawn from representative trainingpopulations, for example, a statistical classifier can be trained toassociate particular patterns in local features with clinical outcomesof interest. For example, a statistical classifier, in this regard, canbe trained with data derived from a particular clinical phase toidentify patterns of local features associated with, and unique to, thatparticular phase. One output of a statistical classifier, for example,can be a classification statistic that is compared with a numericalthreshold to yield a final decision, e.g., whether or not a patient isin an intubation phase. For example, a classifier producing an outputthat is less than a numerical threshold “t” may classify the localfeatures as belonging to an intubation phase, while producing an outputthat exceeds the threshold “t” may result in classifying the localfeatures as in a non-intubated phase. These outcomes can then be storedas process parameters in a memory (not shown). In any event, suitablestatistical classifiers can be used.

To perform statistical classifier techniques, an adequate developmentdatabase of clinical data should be available. FIG. 2 illustrates arepresentative method to obtain the development database and extractknown data patterns. With appropriate device interfaces in place, datacan be collected in a step 126 (preferably via data collection software)from at least one or more devices in an operating suite to build adatabase comprising many surgical cases. As respectively represented bysteps 128 and 130, such a database may include, for example, start andstop times of surgical phases and/or interventions, as determined by aclinician 124 or the like, as well as other relevant surgicalinformation, such as patient demographics, fluids and drugsadministered, labwork, and the like, and any other measurements takenduring a surgery for a particular surgical case, etc. The database canthen be divided into the data collected during each surgical phaseand/or intervention. The database can also be divided further accordingto information known about the case, e.g., types of surgery, surgicalprotocols, patient demographics, etc. Thereafter, a step 132 canidentify key features associated with each surgical phase and/orintervention, such as by using principal component analysis. Once datais appropriately grouped, classifier techniques, or the like, can beperformed at a step 134 to identify distinct data patterns in eachsurgical phase and/or intervention. Data patterns may consist of, forexample, timing information, common trends, absolute values, noisecharacteristics observed from vital signs during induction ormaintenance, etc. For example, one may discover that a rise in bloodpressure in the range of 5-20 mmHg within the first 20 minutes of asurgery may consistently coincide with intubation. Once classifiertechniques are formed, development can include obtaining desired alarmbehavior for each surgical phase and/or intervention, as represented bya step 136. Likewise, desired instrument behavior, UI behavior, andelectronic documentation can also be obtained for each surgical phaseand/or intervention, as respectively depicted in steps 138, 140, and142. Thereafter, data patterns obtained from the development databasecan be used to validate performance against a validation database,acquired during real-time surgery, for example, as depicted in a step144.

The data patterns identified through classifier techniques can drive thedevelopment of an expert system capable of identifying these patterns inreal time. Although numerous expert systems can be used, implementationusing fuzzy logic will be described. Fuzzy logic systems depend onvarious rules that can be evaluated in real-time based on incoming data.Oftentimes, these rules can be structured as if/then statements, e.g.,“If A and/or B and/or C . . . then D.” In implementing surgical phaseand/or intervention detection, the discovered statistical patternsand/or heuristic classifications can be translated into rules, e.g., “Ifblood pressure is rising quickly and surgery just started, then thesurgical phase is induction.” In this statement, the terms “risingquickly” and “just started” are fuzzy, meaning there is a range ofvalues that one could classify as “rising quickly” or “just started,” asopposed to a specific number. The results from the statistical and/orheuristic techniques can be used to define the appropriate ranges foreach of these “fuzzy” terms. Accordingly, fuzzy logic can be well-suitedto surgical phase and/or intervention detection, as different patientcases tend to be unique, e.g., the rate at which blood pressure risesduring intubation can vary across patients. Thus, fuzzy logic allows forcase-to-case analysis and variability.

With an established set of expert rules, the input features to a fuzzylogic system can be defined. For example, if the rules includestatements about changes in blood pressure and time into surgery, thensuch a fuzzy system can be provided with blood pressure trends andsurgical time in order to evaluate these rules. These features candefine the signal processing that can be done before acquired data canbe passed to a fuzzy logic system. Signals can then be processed toprovide features such as data trends, noise content, integratedinformation, etc.

Signal processing, followed by fuzzy logic interpretation, can be usedto identify the transition from one surgical phase and/or interventionto the next. To accomplish this, the fuzzy logic system could considerthe current phase, evaluate rules based on incoming data, and determinewhether or not the data is indicative of a next phase and/orintervention, as depicted in FIG. 3. If, according to the expert rules,the data is characteristic of a next phase and/or intervention, then thesystem 100 can be identified as having detected a transition point.Otherwise, the system 100 can assume the surgical phase and/orintervention has not changed.

Since surgical phases and/or interventions tend to follow generallyconsistent time sequences, e.g., induction before maintenance, etc.,algorithms can be preferably modeled as state systems, whereby differentstates represent different surgical phases and/or interventions andtransitions are driven by fuzzy logic output. In FIG. 4, for example,affected features, such as possible mean blood pressure threshold alarmlimits, possible user interface layouts, and possible non-invasive bloodpressure cycling times can be considered for each surgical phase and/orintervention. Furthermore, multiple state transition models can bestored in a memory (not shown), each corresponding to a particularsurgery type, protocol, patient demographics, etc.

Alternative methods for phase and/or intervention determinations includeneural networks and Hidden Markov Model (HMM) techniques. Because of thewell-known surgical state transitions and highly suggestive observablecharacteristics of different surgical phases and/or interventions, theHMM technique may be particularly well-suited.

FIG. 5 illustrates one example of a flow diagram representing a possiblereal-time implementation of a surgical phase and/or interventiondetection system 100 during a particular surgical procedure. Ifclinician input is not available, then the system 100 can acquire datafrom the patient monitor 112, such as blood pressure, heart rate, oxygenlevel, etc., at a step 146. For enhanced confidence, it can also bedesirable to acquire anesthesia machine 120, IV pump 122, and electronicrecord system 118 data, if possible, in respective steps 148, 150, and152. Thereafter, physiological signals can be signal processed atrespective steps 154 and 156 to remove noise and/or calculate keyfeatures. These physiological signals can also be classified at a step158 by comparing relationships and/or trends to known data patterns.Based on available inputs, a determination can then be made in theconfidence of the surgical classification, at a step 160.

If it is determined, at a step 162, that the surgical phase and/orintervention is the same as previously detected, then the detectionprocess can be repeated, as shown at a step 164. If the surgical phaseand/or intervention is different from the previously detected phaseand/or intervention, then a determination can be made, at a step 166, asto whether the confidence is above a predefined threshold. If so, thenthe newly determined surgical phase and/or intervention can be stored asthe previous phase and/or intervention, at a step 168. In due course,the system 100 can invoke a clinically desired alarm, instrument, and/orUI behavior, respectively at steps 170, 172, and 174, and invokeclinically desired electronic documentation at a step 176, as desired,then repeat the process at a step 178.

If the confidence obtained at step 166 is not above a predefinedthreshold, then a clinician can confirm the detected phase and/orintervention at a step 180. If the clinician does not agree with thedetected phase and/or intervention at a step 182, for example, then theprocess can be repeated at step 164. Alternatively, if the cliniciandoes agree with the detected phase and/or intervention at step 182, thenthe process can revert to steps 168-178.

Also, if a clinician can determine surgical phases and/or interventions,then steps 168-178 can be carried out, particularly at the time when itis determined that clinician input is available.

Identification of the surgical phases and/or interventions can also bedisplayed on the patient monitor 112 (see FIG. 1). An example ofautomated detection of surgical phases and/or interventions is shown inFIG. 6 for an early stage of surgery. Here, an initial fall in systolicpressure, mean arterial pressure (MAP), diastolic pressure, and heartrate, then followed by an increase in each, can signify that intubationfollowed induction.

The present invention enables interpreting patient data in light of agiven surgical context. This presents an opportunity to improve patientalarms and the decisions of support systems, as well as automate,standardize, and/or elaborate electronic record keeping. In addition,optimizing monitoring protocols can also occur for individual surgicalcontexts.

Once the time periods associated with a surgical procedure and/or phasesand/or interventions are determined by a clinician 124 or the like insteps 128 and 130 when obtaining the development database and extractingknown data patterns in FIG. 2, these data points can be used with theinventive arrangements. For example, the detector 114 may be able toautomatedly detect the start and end time of a surgical procedure and/orphases and/or interventions thereof.

Preferably, these time periods can be determined by comparing currenttiming information with predetermined and/or stored timing informationand/or data patterns. For example, if the detector 114 begins receivinginformation from the patient monitor 112 and/or electronic record system118 and/or anesthesia machine 120 and/or IV pump 122, it may determinethat a particular surgical procedure and/or phase and/or interventionhas begun. It may also be able to determine, for example, a length oftime of the surgical procedure and/or phases and/or interventions. Thus,start and end times of the surgical procedure and/or phases and/orinterventions can be based, at least in part, on information receivedfrom the patient monitor 112 and the like. This can also extend todetermining time periods associated with a particular session on aparticular device, such as the anesthesia machine 120 and/or IV pump122. Start and end times associated therewith, for example, can bedetermined for various sessions on such devices.

For example, if the detector 114 does not receive any information from aparticular device for a period of time, it may conclude that the deviceis no longer associated with the patient 100. Or, if the detector hasnot received any information from a particular device for a period oftime and then starts doing so, it may conclude that the device is nowassociated with a patient 100. Depending on the way the timing patternsare established, the system 100 may be able to determine, for example,if a new patient 100 has been introduced to the system 100 or if aprevious patient 100 is undergoing another part of a surgical procedureand/or phase and/or intervention. In other words, the presence orabsence of data for particular time periods can lead the detector 114 tomake determinations about the surgical procedure in general and/or thephases and/or interventions of the surgical procedure and/or sessionsassociated with the surgical procedure and/or particular pieces ofmedical equipment.

It should be readily apparent that this specification describesillustrative, exemplary, representative, and non-limiting embodiments ofthe inventive arrangements. Accordingly, the scope of the inventivearrangements are not limited to any of these embodiments. Rather,various details and features of the embodiments were disclosed asrequired. Thus, many changes and modifications—as readily apparent tothose skilled in these arts—are within the scope of the inventivearrangements without departing from the spirit hereof, and the inventivearrangements are inclusive thereof. Accordingly, to apprise the publicof the scope and spirit of the inventive arrangements, the followingclaims are made:

1. A surgical monitoring device, comprising: a detector operable in electronic communication with a patient monitor to automatedly determine at least one or more time periods associated with at least one or more of a surgical phase, surgical intervention, or both, based, at least in part, on information received from said monitor.
 2. The device of claim 1, wherein said time periods include at least one or more or both of a start time and end time.
 3. The device of claim 2, wherein said detector is operable to determine said time periods based, at least in part, on one or more comparisons to one or more predetermined data patterns.
 4. The device of claim 3, wherein said data patterns are based, at least in part, on one or more of a heuristic or statistical classification.
 5. The device of claim 4, wherein said detector is operable in electronic communication with other medical equipment to determine said time periods based, at least in part, on information received from said other medical equipment.
 6. A surgical monitoring method, comprising: automatedly determining at least one or more time periods associated with at least one or more of a surgical phase, surgical intervention, or both, based, at least in part, on information received from a patient monitor.
 7. The method of claim 6, wherein said time periods include at least one or more or both of a start time and end time.
 8. The method of claim 7, wherein said determining comprises comparing said information with one or more predetermined data patterns.
 9. The method of claim 8, wherein said data patterns are based, at least in part, on one or more of a heuristic or statistical classification.
 10. The method of claim 9, wherein said determining comprises determining said time periods based, at least in part, on information received from other medical equipment.
 11. A surgical monitoring system, comprising: a patient monitor; and a detector operable in electronic communication with said monitor to automatedly determine at least one or more time periods associated with at least one or more of a surgical phase, surgical intervention, or both, based, at least in part, on information received from said monitor.
 12. The system of claim 11, wherein said time periods include at least one or more or both of a start time and end time.
 13. The system of claim 12, wherein said detector is operable to determine said time periods based, at least in part, on one or more comparisons to one or more predetermined data patterns.
 14. The system of claim 13, wherein said data patterns are based, at least in part, on one or more of a heuristic or statistical classification.
 15. The system of claim 14, wherein said detector is operable in electronic communication with other medical equipment to determine said time periods based, at least in part, on information received from said other medical equipment.
 16. A surgical monitoring system, comprising: a patient monitor; a detector operable in electronic communication with said monitor to automatedly determine at least one or more time periods associated with at least one or more of a surgical phase, surgical intervention, or both, based, at least in part on information received from said monitor; and a controller operable to invoke at least one or more responses, based, at least in part, on said time periods.
 17. The system of claim 16, wherein said time periods include at least one or more or both of a start time and end time.
 18. The system of claim 17, wherein said detector is operable to determine said time periods based, at least in part, on one or more comparisons to one or more predetermined data patterns.
 19. The system of claim 18, wherein said data patterns are based, at least in part, on one or more of a heuristic or statistical classification.
 20. The system of claim 19, wherein said detector is operable in electronic communication with other medical equipment to determine said time periods based, at least in part, on information received from said other medical equipment. 